Understand MCP

Anthropic's Model Context Protocol (MCP), open-sourced last November, is rapidly gaining traction as a key middleware layer for Agentic AI. Think of it as a "USB-C" for AI applications, providing a standardized way for AI models to interact with external tools, data sources, and services.

Why is MCP important? Before MCP, integrating AI models with various data sources (file systems, databases, etc.) required custom integrations and APIs – time-consuming and difficult to scale. MCP offers a unified protocol, simplifying this process and improving the LLM user experience. In the first quarter since its release, MCP has seen explosive growth among developers, becoming a dominant middleware between AI Apps/Agents and Tools/Data Sources.

The Growing MCP Ecosystem:

The A16Z market map (see image below) illustrates the burgeoning MCP ecosystem. It's not just developer adoption; we're seeing the emergence of dedicated MCP Clients, Servers, Marketplaces, and Infrastructure.

Key Concepts:

MCP Client: LLM-native products or agents (e.g., Claude Desktop, IDEs like Cursor) that use MCP to access data and tools. A single client can connect to multiple servers.

MCP Server: Lightweight software that acts as a translation interface, allowing LLMs to understand and interact with specific data sources or tools (e.g., file systems, databases, APIs). Think of it as an open, extensible version of GPTs.

Use Cases:

MCP's applications are diverse, spanning databases, search, design, payments, and productivity tools. Search and data retrieval are currently the most frequent use cases. The development model is community-driven, with enterprises increasingly releasing official versions of MCP Servers.

MCP: The Android of Agentic AI?

MCP's open and flexible nature contrasts with more closed approaches like OpenAI's Agent SDK. While Agent SDKs offer fine-grained control for developers building agents, MCP excels when integrating with external tools and data sources without requiring custom development. It's ideal for users who want to leverage the power of tools, not necessarily build them.

MCP is still early, with challenges like scaling server deployments and expanding beyond local-first, single-user setups. However, new marketplaces and hosting solutions are emerging, promising to further boost MCP's adoption and impact. The future of agentic AI may well be built on this open standard.